Dates for oral exams (2nd try):
Data Mining II, CRMRECSYS - January 29 and February 5, 2018
Please register at the Examinations Office
Best Paper Award (2. Position) at the Int. Conf. on Biomedical and Health Informatics (ICBHI 2017) for the Master DKE students Sourabh Dandage, Johannes Huber and Atin Janki: their paper
“Patient Empowerment through summarization of discussion threads on treatments in a patient self-help forum”
Authors: Sourabh Dandage, Johannes Huber, Atin Janki, Uli Niemann, Ruediger Pryss, Manfred Reichert, Steve Harrison, Markku Vessala, Winfried Schlee, Thomas Probst and Myra Spiliopoulou
is a followup of their teamproject on "How patients talk about their tinnitus". Link: here
IEEE Int. Conf. on Data Mining, New Orleans, Louisianna
- Tutorial by Myra Spiliopoulou with Panagiotis Papapetrou on "Mining Cohorts & Patient Data: Challenges and Solutions, for the Pre-Mining, the Mining and the Post-Mining Phases" Nov. 20, 2017
- Panel organized by Myra Spiliopoulou and Naren Ramakrishnan on "Ethics & Professionalism in the age of Social Data" with Huan Liu, Tanushree Mitra, Eirini Ntoutsi and Jilles Vreeken as panelists, Nov. 21, 2017
IEEE ICDM 2017, New Orleans - Tutorial on Mining Cohorts and Patient Data, Monday November 20, 10:30 - 13:00 (NEW DATE)
Myra Spiliopoulou and Georg Krempl will present a Tutorial on Mining Multiple Threads of streaming Data at PAKDD 2013, April 14-17, Gold Coast, Australia.
Stream mining is a mature area of research. However, several applications that require adaptive learning from evolving data do not seem to fit to the conventional stream mining paradigm. For example, a bank grants loans to customers and uses their data for model learning; the label (loan-payed-back YES or NO) arrives some years later, though, during which years the market may have changed drastically. Is this a stream mining problem? How many streams are there? We can distinguish between the stream of customers and the stream of their labels, which arrive with a time lag of years.
As another example, a hospital monitors patients with chronical diseases that come (ir)regularly to the hospital and undergo different tests; the streams of medical recordings and of signals (EEG, fMRI) can be used for learning. The hospital wants to learn a model on how the patients' health evolves in response to the disease and to medications. This problem seems completely different from the previous one, albeit streams of data are there in both cases.
In this tutorial, Myra Spiliopoulou and Georg Krempl bring together research advances on model learning and adaption for dynamic applications that collect and analyze different sources of dynamic data. In the introductory part of the tutorial, they present the classic stream mining paradigm and summarize the challenges being investigated in the state-of-the-art research.